Automatic assignment of biomedical categories: toward a generic approach
Automatic assignment of biomedical categories: toward a generic approach
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DOI：
10.1093/bioinformatics/bti783
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发表时间：
2006-03-15
期刊：
影响因子：
5.8
通讯作者：
Ruch, P
中科院分区：
文献类型：
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作者：
Ruch, P
动机：我们报告一个通用的文本分类系统，旨在自动分配生物医学类别的任何输入文本的发展。不像通常的自动文本分类系统，它依赖于从大量的训练数据集提取的数据密集型模型，我们的分类器在很大程度上是独立于data-independent.Methods：为了评估我们的方法的鲁棒性，我们测试系统上两个不同的生物医学术语：医学主题词（MeSH）和基因本体论（GO）。我们的轻量级分类器，基于两个排名模块，结合了模式匹配器和向量空间检索引擎，并使用词干和语言驱动的索引单元。结果和结论：结果显示了短语索引对GO和MeSH分类的有效性，但我们观察到该工具的分类能力取决于受控词汇：高等级的精确度范围从MeSH的90%以上到GO的< 20%，为基于检索方法的分类器建立了新的基线。
Motivation: We report on the development of a generic text categorization system designed to automatically assign biomedical categories to any input text. Unlike usual automatic text categorization systems, which rely on data-intensive models extracted from large sets of training data, our categorizer is largely data-independent.Methods: In order to evaluate the robustness of our approach we test the system on two different biomedical terminologies: the Medical Subject Headings (MeSH) and the Gene Ontology (GO). Our lightweight categorizer, based on two ranking modules, combines a pattern matcher and a vector space retrieval engine, and uses both stems and linguistically-motivated indexing units.Results and Conclusion: Results show the effectiveness of phrase indexing for both GO and MeSH categorization, but we observe the categorization power of the tool depends on the controlled vocabulary: precision at high ranks ranges from above 90% for MeSH to < 20% for GO, establishing a new baseline for categorizers based on retrieval methods.